Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add sethdford/claude-skills --skill storage-selectiongit clone --depth 1 https://github.com/sethdford/claude-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sethdford/claude-skills/storage-selection)<a href="https://agentmods.dev/skills/sethdford/claude-skills/storage-selection"><img src="https://agentmods.dev/badge/skills/sethdford/claude-skills/storage-selection/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sethdford/claude-skills/storage-selection"><img src="https://agentmods.dev/badge/skills/sethdford/claude-skills/storage-selection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00038 | $0.00716 |
| Opus 5 | $0.00019 | $0.00358 |
| Sonnet 5 | $0.00008 | $0.00143 |
| Haiku 4.5 | $0.00004 | $0.00072 |
Grade A, and why
storage-selection scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Storage Selection
Choose optimal database technology by analyzing workload characteristics, consistency requirements, and operational complexity.
Context
You are helping select storage systems for specific data requirements. Analyze access patterns, consistency needs, data volume, and operational constraints. Read any existing schemas or architectural context provided.
Domain Context
Based on Martin Kleppmann's Designing Data-Intensive Applications:
- Relational Databases: ACID transactions, schema enforcement, complex queries; limited horizontal scaling
- Document Databases: Flexible schema, natural object mapping, good for hierarchical data; transactions often single-document only
- Key-Value Stores: Fast reads/writes, simple data model; limited query flexibility
- Column Family Stores: Optimized for analytics and time-series; sparse data; distributed
- Search Engines: Full-text search, fuzzy matching, aggregations; not transaction-oriented
- Data Warehouses: Analytical queries, columnar storage, massive scale; designed for batch not realtime
Instructions
-
Catalog Data Requirements: List primary access patterns (read-heavy? write-heavy? both?), data volume growth rate, consistency needs (strong/eventual?), query types (transactional vs analytical), and operational SLAs.
-
Match Workload to Storage Type: Transactional workload with complex queries → PostgreSQL. Document-oriented objects → MongoDB. Time-series metrics → Prometheus or InfluxDB. Full-text search → Elasticsearch. Analytical queries on massive datasets → BigQuery/Redshift.
-
Evaluate Consistency vs Availability: Need ACID guarantees across distributed systems? Relational with replication. Can tolerate eventual consistency? DynamoDB/Cassandra. Determine RTO/RPO requirements.
-
Project Operational Burden: Managed services (RDS, DynamoDB, BigQuery) reduce ops overhead. Self-hosted databases require backup, monitoring, scaling, tuning expertise.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 49 lines · 38 tokens per session scan A 4189d7af9252
storage-selection is a skill published in the GitHub repository sethdford/claude-skills (39 stars, last pushed 6mo ago), licensed MIT. It adds 38 tokens to every session and 716 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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